Study archive · AI simulation with synthetic data · Human validation pending

Fictional assumed workflow — P04

Samira Patel is a fictional AI study persona acting as a market analysis specialist at a hypothetical specialty-software product team. This document describes assumptions, not a real participant's experience.

• Assumed frequency: Fortnightly market-signal triage.
• Assumed tools: A reimbursement report, a sales report, terminal-based file inspection, and a structured memo. The app condition uses a coordinator-captured app artifact; this exercise does not measure browser usability.
• Assumed steps: Review the condition's task and metric definitions; inspect its provided report or app evidence; compare reimbursement and sales signals at the supported grain; independently check material calculations; identify limitations; draft discovery questions with their supporting evidence and links.
• Expected handoff: A concise evidence-bounded brief for Theo Grant, the fictional product manager, distinguishing observations, hypotheses, questions, and missing evidence.
• Pain-point hypotheses: Reports may use different periods, definitions, or geographic grains; small samples may make large ratios misleading; descriptive reimbursement signals may be mistaken for product demand; evidence and source links may be difficult to keep together. These are hypotheses requiring validation, not observed user pain.
• Downstream decision: Theo decides which discovery questions need further evidence before any product or commercial recommendation.
• Unknowns requiring real-user confirmation: Actual review frequency, tools and source access, ownership of data checks, relevant markets and customer segments, acceptable evidence thresholds, expected memo format, review effort, and how discovery questions are prioritized.

No historical durations, real experience, customer behavior, or realized impact are claimed. Recorded timing is descriptive AI runtime only.